Implementation and assessment of a pilot, community pharmacy-based, opioid pain medication management program.

OBJECTIVES: This study aimed to evaluate care gaps in risk- and harm-reduction strategies for patients prescribed opioids and to describe the implementation of a community pharmacy-based, pilot pain-management program. SETTING: The pilot program was established in a community pharmacy within an academic medical center. Patients enrolled were prescribed opioids for chronic pain by a rheumatology clinic. PRACTICE DESCRIPTION: The patients enrolled met 1 or more of the following criteria: they were prescribed more than 1 short-acting opioid; more than 90 morphine milligram equivalents/d; and more than 7 days' supply of medications for acute pain, including high-risk medication combinations. Comprehensive pain-medication assessments and pharmacist interventions were communicated to providers and implemented at follow-up. Data were analyzed using descriptive statistics. PRACTICE INNOVATION: A gap analysis was conducted by including 23 patients seen at the clinic over a 22-month period. The care gaps identified served as the basis for the pilot-program design. EVALUATION: Patients referred to the program were seen over a span of 1 to 2 visits; a total of 19 visits were documented. Pharmacists identified unaddressed issues with mood (68%). Recommendations made to the providers included additional adjuvant therapy (84%), dose adjustment (58%), and laboratory tests (74%). Naloxone was provided (58%), and education on naloxone use was provided at every visit. DISCUSSION: Untreated depression, anxiety, and insomnia were the most common problems identified by pharmacists. Pharmacists implemented and documented risk-reduction strategies and coprescribed naloxone more frequently compared with clinic providers. The program enhanced the pharmacists' ability to make safe and clinically appropriate decisions with regard to filling opioid prescriptions. CONCLUSION: The pilot program identified care gaps and provided an approach for engaging with patients and providers to optimize pain management, implement opioid risk-reduction strategies, and expand naloxone access.

Microbial signals, MyD88, and lymphotoxin drive TNF-independent intestinal epithelial tissue damage.

Anti-TNF antibodies are effective for treating patients with inflammatory bowel disease (IBD), but many patients fail to respond to anti-TNF therapy, highlighting the importance of TNF-independent disease. We previously demonstrated that acute deletion of 2 IBD susceptibility genes, A20 (Tnfaip3) and Abin-1 (Tnip1), in intestinal epithelial cells (IECs) sensitized mice to both TNF-dependent and TNF-independent death. Here we show that TNF-independent IEC death after A20 and Abin-1 deletion was rescued by germ-free derivation or deletion of MyD88, while deletion of Trif provided only partial protection. Combined deletion of Ripk3 and Casp8, which inhibits both apoptotic and necroptotic death, completely protected against death after acute deletion of A20 and Abin-1 in IECs. A20- and Abin-1-deficient IECs were sensitized to TNF-independent, TNFR1-mediated death in response to lymphotoxin α (LTα) homotrimers. Blockade of LTα in vivo reduced weight loss and improved survival when combined with partial deletion of MyD88. Biopsies of inflamed colon mucosa from patients with IBD exhibited increased LTA and IL1B expression, including a subset of patients with active colitis on anti-TNF therapy. These data show that microbial signals, MyD88, and LTα all contribute to TNF-independent intestinal injury.

Phytosterol accumulation results in ventricular arrhythmia, impaired cardiac function and death in mice.

Heart failure (HF) and cardiac arrhythmias share overlapping pathological mechanisms that act cooperatively to accelerate disease pathogenesis. Cardiac fibrosis is associated with both pathological conditions. Our previous work identified a link between phytosterol accumulation and cardiac injury in a mouse model of phytosterolemia, a rare disorder characterized by elevated circulating phytosterols and increased cardiovascular disease risk. Here, we uncover a previously unknown pathological link between phytosterols and cardiac arrhythmias in the same animal model. Phytosterolemia resulted in inflammatory pathway induction, premature ventricular contractions (PVC) and ventricular tachycardia (VT). Blockade of phytosterol absorption either by therapeutic inhibition or by genetic inactivation of NPC1L1 prevented the induction of inflammation and arrhythmogenesis. Inhibition of phytosterol absorption reduced inflammation and cardiac fibrosis, improved cardiac function, reduced the incidence of arrhythmias and increased survival in a mouse model of phytosterolemia. Collectively, this work identified a pathological mechanism whereby elevated phytosterols result in inflammation and cardiac fibrosis leading to impaired cardiac function, arrhythmias and sudden death. These comorbidities provide insight into the underlying pathophysiological mechanism for phytosterolemia-associated risk of sudden cardiac death.

Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet.

Multiple research groups have shown that diet impacts the gut microbiome; however, variability in experimental design and quantitative assessment have made it challenging to assess the degree to which similar diets have reproducible effects across studies. Through an unbiased subject-level meta-analysis framework, we re-analyzed 27 dietary studies including 1,101 samples from rodents and humans. We demonstrate that a high-fat diet (HFD) reproducibly changes gut microbial community structure. Finer taxonomic analysis revealed that the most reproducible signals of a HFD are Lactococcus species, which we experimentally demonstrate to be common dietary contaminants. Additionally, a machine-learning approach defined a signature that predicts the dietary intake of mice and demonstrated that phylogenetic and gene-centric transformations of this model can be translated to humans. Together, these results demonstrate the utility of microbiome meta-analyses in identifying robust and reproducible features for mechanistic studies in preclinical models.